Financial institutions commonly apply algorithms across vast amounts of transaction data to identify large-scale customer-behavior patterns. Then, applying these large-scale customer-behavior patterns in models, financial institutions identify opportunities to interact with customers, as a whole, with more precision and at times that are more likely to produce positive results. However, these large-scale customer-behavior patterns are not useful for identifying opportunities to interact with customers at an account level.
Although known pattern-detection techniques are effective for detecting large-scale customer-behavior patterns in aggregated transaction data taken from a large number of customers, known pattern-detection techniques are ineffective for detecting customer-behavior patterns at an account level. This ineffectiveness is particularly true in the area of detecting customer-behavior patterns—at an account level—in aggregated transaction data taken from revolving-credit accounts, such as credit card accounts.
There are a number of reasons for this ineffectiveness. For example, when multiple customers' credit card spending and balance data are aggregated, patterns fail to emerge because the aggregated data is offsetting. That is, for each customer with high spend in a month, there's a customer with proportionately low spend in the same month. Likewise, for each customer with a high balance, there's a customer with a proportionately low balance. Accordingly, no patterns emerge because the average amount customers spend (e.g., charge to a credit card) in a month is essentially the same each month and the average balance (e.g., outstanding balance on a credit card) is the same each month.
However, common knowledge tells us—hidden in vast amounts of credit card spending and balance data—are subsets of individual customers whose credit card spending and balance fluctuate from month to month according to similar patterns. If identified, these patterns would reflect individual customers' decisions on how much to spend, and how much and when to payback. These patterns would be helpful when evaluating individual customers' ability to manage revolving credit. However, because of the reasons described above, known pattern-detection techniques are ineffective for detecting these patterns.